With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient (R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

中文翻译：

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基于肌力图信号估计膝关节加速度的长短期记忆神经网络模型

随着患有运动功能障碍的老年人和残疾人数量的增加，对辅助运动的需求也在增加。可穿戴式动力辅助机器人旨在为老年人或肢体虚弱的残疾人提供肢体运动能力，以提高自理能力。现有的可穿戴动力辅助机器人通常使用表面肌电图（sEMG）来获取有效的人体运动意图。由于 sEMG 信号的特性，它在许多应用中受到限制。为解决上述问题，我们设计了一种基于人体肌力图（MMG）信号的长短期记忆（LSTM）神经网络模型来估计膝关节的运动加速度。加速度可以进一步通过下肢可穿戴式助力机器人运动控制所需的扭矩计算得出。我们检测衣服大腿上的 MMG 信号，提取 MMG 信号的特征，然后使用主成分分析来减少特征的维度。最后，将降维特征按时间序列输入到 LSTM 神经网络模型中，用于估计加速度。实验结果表明，在近似π/4 rad/s下连续执行过程中的加速度估计的平均相关系数（R）为94.48±1.91%。这种方法可以应用于可穿戴领域的实际应用。实验结果表明，在近似π/4 rad/s下连续执行过程中的加速度估计的平均相关系数（R）为94.48±1.91%。这种方法可以应用于可穿戴领域的实际应用。实验结果表明，在近似π/4 rad/s下连续执行过程中的加速度估计的平均相关系数（R）为94.48±1.91%。这种方法可以应用于可穿戴领域的实际应用。